Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover read-only, open-world, idempotent, and non-destructive traits, so the description's additional disclosures go well beyond the bar: account/plan requirements, parallel decomposition, gap reporting with 'never invented', contradiction scans, timing expectations, semantic excerpting, and fetchable citation URIs. No contradiction with the annotations was found.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Every sentence contributes substantive behavioral or routing information, and the most decision-critical caveats are front-loaded. However, the description is a dense single block with frequent semicolons, which makes it harder to scan quickly than a structured or shorter version would be.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the full burden of documenting the return value and edge cases, and it does so thoroughly: findings structure, verbatim evidence, confidence, source, fetched_at, gaps[], contradictions[], citation reachability, and latency. Given the tool's complexity, an agent has enough information to select it and call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema coverage is 100%, so the baseline is 3, but the description enriches both parameters: it explains what the depth levels actually do in the research flow (quick=3, standard=3 with gap recovery, thorough=6 paid with iterative chase) and confirms that the question parameter is meant for natural-language, multi-part queries. This adds practical selection guidance beyond the schema's enum text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources' in one call. It also explicitly distinguishes itself from open-web search and from ask_pipeworx, making the tool's role among siblings clear. The return artifact — a findings packet with citations and gaps — is also named.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use guidance: best for broad/multi-part questions over structured data, and 'For a single lookup use ask_pipeworx instead.' It also names the alternative for signed-out users and explains depth-tier implications. This is concrete routing behavior, not vague context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.